As the processing power of computers allow for greater computer functionality and the Internet technology era allows for interconnectivity between computing systems, more records of data are generated, stored, maintained, and queried every day. As a result, the size and number of datasets and databases available continues to grow and expand exponentially. The datasets and records within these databases may be generated in a variety of ways, from a variety of related or unrelated sources. Furthermore, the datasets and records may be generated at different times, and stored in different formats and locations. As a result, problems occur when users try to query large seemingly unrelated datasets because the relationships between the data records stored in the unrelated databases and records may not be obvious since the records may be stored in different formats or related to different entities and subject matters that do not share common identifiers.
Conventionally, querying different datasets has been accomplished using a “brute force” method of analyzing all datasets and databases. Existing and conventional methods fail to provide fast and efficient analysis due to a high volume of data existing on different networks and computing infrastructures. Managing and organizing such data on different platforms is difficult due to number, size, content, or relationships of the data within a database. Furthermore, existing and conventional methods consume a large amount of computing power, which is not ideal.